High-Energy Physics

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25 papers in the last four weeks, up 39% on the four weeks before. 0.1% of all new papers.

Forecasting Generative Amplification
Sep 9, 2025Henning Bahl, Sascha Diefenbacher, Nina Elmer +2Generative Modeling High-Energy Physics
Institut für Theoretische Physik, Universität Heidelberg, Germany · Physics Division, Lawrence Berkeley National Laboratory, Berkeley, USA · Interdisciplinary Center for Scientific Computing (IWR), Universität Heidelberg, Germany

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Forecasting Generative Amplification

Henning Bahl, Sascha Diefenbacher, Nina Elmer, Tilman Plehn+1

Institut für Theoretische Physik, Universität Heidelberg, Germany · Physics Division, Lawrence Berkeley National Laboratory, Berkeley, USA · Interdisciplinary Center for Scientific Computing (IWR), Universität Heidelberg, Germany

Generative Modeling High-Energy Physics Statistical Hypothesis Testing

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Generative networks are perfect tools to enhance the speed and precision of LHC simulations. Especially when generating events beyond the size of the training dataset, it is important to understand their statistical precision. We present two complementary methods to estimate the amplification factor without large holdout datasets. Averaging amplification uses Bayesian networks or ensembling to estimate amplification from the precision of integrals over given phase-space volumes. Differential amplification uses hypothesis testing to quantify amplification without any resolution loss. Applied to state-of-the-art event generators, both methods indicate that amplification is already possible in specific regions of phase space.